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Sponsored Artificial Intelligence: A Modern Approach, Global Edition Artificial Intelligence: A Modern Approach, Global Edition

Author

L. Zimmermann

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4 papers indexed here

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Preprint Sep 2026

PyDoseRT Photon: Physics-Guided Pencil-Beam Dose Calculation with Neural Priors and Residual Correction for CT and MRI

We present a hybrid, physics-based analytical pencil-beam (PB) dose engine, augmented by two small frozen neural physics priors, followed by a 3-D convolutional residual-correction network (U-Net). We address the DoseRAD2026 (https://doserad2026.grand-challenge.org/) photon dose-prediction task with PyDoseRT Photon, a...

A. Simkó, L. Zimmermann, H. Fuchs et al. · 0 citations
Open access Aug 2026

Eliminating registration bias in synthetic CT generation using a physics-based simulation framework for pelvic anatomy

Objective. Supervised synthetic computed tomography (sCT) generation from cone-beam computed tomography (CBCT) requires spatially registered training pairs, yet perfect registration between separately acquired scans is unattainable. This registration bias propagates into trained models and corrupts intensity-based eval...

L. Zimmermann, Michael Rauter, Martin Buschmann et al. · 0 citations
Preprint Sep 2026

PyDoseRT Proton: A GPU Pencil-Beam Engine with a Convolutional Residual-Correction Network for Fast Proton Dose Calculation

Architecture category. Hybrid method: a physics-based analytical pencil-beam (PB) dose engine followed by a 3-D convolutional residual-correction network (RepVGG-U-Net). We addressed the DoseRAD2026 proton dose-prediction task with PyDoseRT Proton, a GPU-accelerated engine implemented in PyTorch and augmented by a lear...

L. Zimmermann, H. Fuchs, A. Simkó et al. · 0 citations
Open access Aug 2026

Eliminating Registration Bias in Synthetic CT Generation using a physics-based simulation framework for pelvic anatomy.

OBJECTIVE Supervised synthetic computed tomography (sCT) generation from cone-beam CT (CBCT) requires spatially registered training pairs, yet perfect registration between separately acquired scans is unattainable. This registration bias propagates into trained models and corrupts intensity-based evaluation, so higher...

L. Zimmermann, Michael Rauter, Martin Buschmann et al. · 0 citations

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